{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_SEQUENCE_LENGTH = 200 # 句子 上限200个词\n",
    "EMBEDDING_DIM = 100 # 100d 词向量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good len: 1294531\n",
      "bad len: 48126\n"
     ]
    }
   ],
   "source": [
    "good = []\n",
    "bad = []\n",
    "for line in open('data/goodqueries.txt'):\n",
    "    good.append(line.strip('\\n'))\n",
    "for line in open('data/badqueries.txt'):\n",
    "    bad.append(line.strip('\\n'))\n",
    "print('good len:', len(good))\n",
    "print('bad len:', len(bad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: 192504\n",
      "/103886/ <svg onload=location='//p0.al'>\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "labels = []\n",
    "\n",
    "length = len(bad)\n",
    "scale = 3\n",
    "data.extend(good[:length * scale]) # 只取部分数据\n",
    "data.extend(bad)\n",
    "labels.extend([1] * length * scale)\n",
    "labels.extend([0] * length)\n",
    "print('data:', len(data))\n",
    "print(data[0], data[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 175 unique tokens.\n",
      "Shape of data tensor: (192504, 200)\n"
     ]
    }
   ],
   "source": [
    "# https://keras-cn-docs.readthedocs.io/zh_CN/latest/blog/word_embedding/\n",
    "import numpy as np\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "# tokenizer\n",
    "texts = data\n",
    "tokenizer = Tokenizer(char_level=True) # 字向量\n",
    "tokenizer.fit_on_texts(texts)\n",
    "word_index = tokenizer.word_index\n",
    "\n",
    "# sequences\n",
    "sequences = tokenizer.texts_to_sequences(data)\n",
    "\n",
    "# padding\n",
    "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "print('Shape of data tensor:', data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "token_path = 'model/tokenizer.pkl'\n",
    "pickle.dump(tokenizer, open(token_path, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# 打乱顺序\n",
    "index = [i for i in range(len(data))]\n",
    "random.shuffle(index)\n",
    "data = np.array(data)[index]\n",
    "labels = np.array(labels)[index]\n",
    "\n",
    "TRAIN_SPLIT = 0.8 # 20% 测试集\n",
    "TRAIN_SIZE = int(len(data) * TRAIN_SPLIT)\n",
    "\n",
    "X_train, X_test = data[0:TRAIN_SIZE], data[TRAIN_SIZE:]\n",
    "Y_train, Y_test = labels[0:TRAIN_SIZE], labels[TRAIN_SIZE:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train len: 154003\n",
      "test len: 38501\n"
     ]
    }
   ],
   "source": [
    "print('train len:', len(X_train))\n",
    "print('test len:', len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\n",
    "\n",
    "G = 1 # GPU 数量\n",
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)\n",
    "session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True))\n",
    "K.set_session(session)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN + Bi-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 200, 100)          17600     \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 198, 128)          38528     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 198, 128)          512       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 198, 128)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 49, 128)           0         \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 128)               98816     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 1)                 4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 164,037\n",
      "Trainable params: 163,651\n",
      "Non-trainable params: 386\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, BatchNormalization\n",
    "from keras.layers import Dense, LSTM, Convolution1D, MaxPooling1D\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers.wrappers import Bidirectional\n",
    "\n",
    "QA_EMBED_SIZE = 64\n",
    "DROPOUT_RATE = 0.3\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(len(word_index) + 1, EMBEDDING_DIM, input_length=MAX_SEQUENCE_LENGTH))\n",
    "model.add(Convolution1D(filters=128, kernel_size=3, padding='valid', activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling1D(4))\n",
    "model.add(Bidirectional(LSTM(QA_EMBED_SIZE, return_sequences=False, dropout=DROPOUT_RATE, recurrent_dropout=DROPOUT_RATE)))\n",
    "\n",
    "model.add(Dense(QA_EMBED_SIZE))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(1))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型可视化 https://keras-cn.readthedocs.io/en/latest/other/visualization/\n",
    "from keras.utils import plot_model\n",
    "from IPython import display\n",
    "\n",
    "# pip install pydot-ng\n",
    "# sudo apt-get install graphviz\n",
    "plot_model(model, to_file=\"img/model-cnn-blstm.png\", show_shapes=True)\n",
    "display.Image('img/model-cnn-blstm.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 107802 samples, validate on 46201 samples\n",
      "Epoch 1/3\n",
      "107802/107802 [==============================] - 333s 3ms/step - loss: 0.2032 - acc: 0.9825 - precision: 0.9905 - recall: 0.9862 - f1: 0.9880 - val_loss: 0.0934 - val_acc: 0.9955 - val_precision: 0.9962 - val_recall: 0.9979 - val_f1: 0.9970\n",
      "Epoch 2/3\n",
      "107802/107802 [==============================] - 325s 3ms/step - loss: 0.0615 - acc: 0.9954 - precision: 0.9971 - recall: 0.9967 - f1: 0.9969 - val_loss: 0.0407 - val_acc: 0.9959 - val_precision: 0.9966 - val_recall: 0.9980 - val_f1: 0.9973\n",
      "Epoch 3/3\n",
      "107802/107802 [==============================] - 320s 3ms/step - loss: 0.0315 - acc: 0.9964 - precision: 0.9980 - recall: 0.9973 - f1: 0.9976 - val_loss: 0.0208 - val_acc: 0.9965 - val_precision: 0.9979 - val_recall: 0.9974 - val_f1: 0.9976\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd0941b59e8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
    "# from keras.utils import multi_gpu_model\n",
    "from evaluate import *\n",
    "\n",
    "EPOCHS = 3\n",
    "BATCH_SIZE = 64 * G\n",
    "VALIDATION_SPLIT = 0.3 # 30% 验证集\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "model_checkpoint = ModelCheckpoint('model/model-cnn-blstm.h5', save_best_only=True, save_weights_only=True)\n",
    "tensor_board = TensorBoard('log/tflog-cnn-blstm', write_graph=True, write_images=True)\n",
    "\n",
    "# model = multi_gpu_model(model)\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy', precision, recall, f1])\n",
    "\n",
    "model.fit(X_train, Y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, \n",
    "          validation_split=VALIDATION_SPLIT, shuffle=True, \n",
    "          callbacks=[early_stopping, model_checkpoint, tensor_board])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38501/38501 [==============================] - 26s 679us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.021407353707455754,\n",
       " 0.9959741305420639,\n",
       " 0.9974619871188918,\n",
       " 0.9971503111632538,\n",
       " 0.9972767950153942]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, Y_test, verbose=1, batch_size=BATCH_SIZE)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
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